Public articles linked to the same research event.
arXiv GRAFT introduces a continual multi-teacher knowledge distillation framework in which the previously distilled model acts as a teacher to preserve learned capabilities while the current student jointly learns from that model and the incoming teacher, and it adds Teacher Specific Readout Tokens plus a Geometry Agnostic Relational Loss to reconcile the incompatible representation geometries of heterogeneous teachers, yielding a single continually extensible backbone that unifies image understanding, 2D dense prediction, 3D human pose estimation, 3D vision, and vision-language, with each new capability acquired at the cost of a single distillation rather than a full re-distillation.
GRAFT introduces a continual multi-teacher knowledge distillation framework in which the previously distilled model acts as a teacher to preserve learned capabilities while the current student jointly learns from that model and the incoming teacher, and it adds Teacher Specific Readout Tokens plus a Geometry Agnostic Relational Loss to reconcile the incompatible representation geometries of heterogeneous teachers, yielding a single continually extensible backbone that unifies image understanding, 2D dense prediction, 3D human pose estimation, 3D vision, and vision-language, with each new capability acquired at the cost of a single distillation rather than a full re-distillation.
GRAFT introduces a continual multi-teacher knowledge distillation framework in which the previously distilled model acts as a teacher to preserve learned capabilities while the current student jointly learns from that model and the incoming teacher, and it adds Teacher Specific Readout Tokens plus a Geometry Agnostic Relational Loss to reconcile the incompatible representation geometries of heterogeneous teachers, yielding a single continually extensible backbone that unifies image understanding, 2D dense prediction, 3D human pose estimation, 3D vision, and vision-language, with each new capability acquired at the cost of a single distillation rather than a full re-distillation.
GRAFT introduces a continual multi-teacher knowledge distillation framework in which the previously distilled model acts as a teacher to preserve learned capabilities while the current student jointly learns from that model and the incoming teacher, and it adds Teacher Specific Readout Tokens plus a Geometry Agnostic Relational Loss to reconcile the incompatible representation geometries of heterogeneous teachers, yielding a single continually extensible backbone that unifies image understanding, 2D dense prediction, 3D human pose estimation, 3D vision, and vision-language, with each new capability acquired at the cost of a single distillation rather than a full re-distillation.